In multi-shot high-resolution MRI, the motion of patients often leads to serious degradation of imaging quality. In this study, a novel motion correction method based on deep learning and spatiotemporally encoded MRI was proposed to address this problem. The proposed method is robust to motion without utilizing extra scan or parallel reconstruction. The results of simulation and in vivo rat brain experiments demonstrate its efficacy in reducing image motion artifacts when subject movement exists.
This abstract and the presentation materials are available to members only; a login is required.